@inproceedings{38d018abdf334c68bb40a9525f9ae0b4,
title = "A system for analyzing criminal social networks",
abstract = "The influential members of a criminal organization are usually targeted by criminal investigators for removal or surveillance. Identifying and capturing these influential members will most likely to disrupt the organization. We propose in this paper a forensic analysis system called CLDRI that can identify the most influential members of a criminal organization. First, a network representing a criminal organization is built from Mobile Communication Data that belongs to the organization. In such a network, a vertex represents an individual criminal and an edge represents the communication attempts between two criminals. CLDRI employs formulas that quantify the degree of importance of each vertex in the network relative to all other vertices. We present these formulas through series of improvement refinements. All the formulas incorporate novel-weighting schemes for the edges of networks. We evaluated the quality of CLDRI by comparing it experimentally with two systems. Results showed improvement.",
keywords = "Criminal network, Digital forensic, Forensic analysis, Mobile communication data, Social network",
author = "Kamal Taha and Yoo, \{Paul D.\}",
note = "Publisher Copyright: {\textcopyright} 2015 ACM.; IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2015 ; Conference date: 25-08-2015 Through 28-08-2015",
year = "2015",
month = aug,
day = "25",
doi = "10.1145/2808797.2808827",
language = "British English",
series = "Proceedings of the 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2015",
pages = "1017--1023",
editor = "Jian Pei and Jie Tang and Fabrizio Silvestri",
booktitle = "Proceedings of the 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2015",
}
